ADS_Project1

Abstract

In today's life, ideology has become an indispensable part of life. It is precisely because of the difference in ideology that our way of dealing with life and the way of thinking about problems are different. Therefore, the quantitative analysis of textual data on philosophy may be a topic worthy of consideration. This article will use python to ask questions about philosophers, philosophical schools, and the philosophical discourse itself and the possible relationships between them. And use the knowledge of data analysis with relevant data to give analysis and answers.

(Key Words: Philosophy, Data Analysis, Python)

Introduction

We may wonder what the ideology is. There are a lot of different kinds of ideologies, including political, social, epistemological, and ethical. Recent analysis tends to posit that ideology is a 'coherent system of ideas' that rely on a few basic assumptions about reality that may or may not have any factual basis. Through this system, ideas become coherent, repeated patterns through the subjective ongoing choices that people make. These ideas serve as the seed around which further thought grows. The belief in an ideology can range from passive acceptance up to fervent advocacy. According to most recent analysis, ideologies are neither necessarily right nor wrong[1]. Based on these ideologies, they form kinds of philosophy schools and ideas.

(1) Import Data

We use pandas to import data and then we show the data to see whether we can find some questions (some relations) to seek.

import numpy as np
import pandas as pd
import random

# plots
import matplotlib.pyplot as plt
import seaborn as sns

# NLP
from wordcloud import WordCloud, STOPWORDS


######################################################################################################################
data = pd.read_csv('data.csv')

data

	title	author	school	sentence_spacy	sentence_str	original_publication_date	corpus_edition_date	sentence_length	sentence_lowered	tokenized_txt	lemmatized_str
0	Plato - Complete Works	Plato	plato	What's new, Socrates, to make you leave your ...	What's new, Socrates, to make you leave your ...	-350	1997	125	what's new, socrates, to make you leave your ...	['what', 'new', 'socrates', 'to', 'make', 'you...	what be new , Socrates , to make -PRON- lea...
1	Plato - Complete Works	Plato	plato	Surely you are not prosecuting anyone before t...	Surely you are not prosecuting anyone before t...	-350	1997	69	surely you are not prosecuting anyone before t...	['surely', 'you', 'are', 'not', 'prosecuting',...	surely -PRON- be not prosecute anyone before ...
2	Plato - Complete Works	Plato	plato	The Athenians do not call this a prosecution b...	The Athenians do not call this a prosecution b...	-350	1997	74	the athenians do not call this a prosecution b...	['the', 'athenians', 'do', 'not', 'call', 'thi...	the Athenians do not call this a prosecution ...
3	Plato - Complete Works	Plato	plato	What is this you say?	What is this you say?	-350	1997	21	what is this you say?	['what', 'is', 'this', 'you', 'say']	what be this -PRON- say ?
4	Plato - Complete Works	Plato	plato	Someone must have indicted you, for you are no...	Someone must have indicted you, for you are no...	-350	1997	101	someone must have indicted you, for you are no...	['someone', 'must', 'have', 'indicted', 'you',...	someone must have indict -PRON- , for -PRON- ...
...	...	...	...	...	...	...	...	...	...	...	...
360803	Women, Race, And Class	Davis	feminism	But the socialization of housework including m...	But the socialization of housework including m...	1981	1981	142	but the socialization of housework including m...	['but', 'the', 'socialization', 'of', 'housewo...	but the socialization of housework include me...
360804	Women, Race, And Class	Davis	feminism	The only significant steps toward endingdomest...	The only significant steps toward endingdomest...	1981	1981	117	the only significant steps toward endingdomest...	['the', 'only', 'significant', 'steps', 'towar...	the only significant step toward endingdomest...
360805	Women, Race, And Class	Davis	feminism	Working women, therefore, have a special and v...	Working women, therefore, have a special and v...	1981	1981	90	working women, therefore, have a special and v...	['working', 'women', 'therefore', 'have', 'spe...	working woman , therefore , have a special an...
360806	Women, Race, And Class	Davis	feminism	Moreover, under capitalism, campaigns for jobs...	Moreover, under capitalism, campaigns for jobs...	1981	1981	199	moreover, under capitalism, campaigns for jobs...	['moreover', 'under', 'capitalism', 'campaigns...	moreover , under capitalism , campaign for jo...
360807	Women, Race, And Class	Davis	feminism	This strategy calls into question the validity...	This strategy calls into question the validity...	1981	1981	126	this strategy calls into question the validity...	['this', 'strategy', 'calls', 'into', 'questio...	this strategy call into question the validity...

360808 rows × 11 columns

Based on the obeservation, the data does not exist garbled.

(2) Data Information

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 360808 entries, 0 to 360807
Data columns (total 11 columns):
 #   Column                     Non-Null Count   Dtype 
---  ------                     --------------   ----- 
 0   title                      360808 non-null  object
 1   author                     360808 non-null  object
 2   school                     360808 non-null  object
 3   sentence_spacy             360808 non-null  object
 4   sentence_str               360808 non-null  object
 5   original_publication_date  360808 non-null  int64 
 6   corpus_edition_date        360808 non-null  int64 
 7   sentence_length            360808 non-null  int64 
 8   sentence_lowered           360808 non-null  object
 9   tokenized_txt              360808 non-null  object
 10  lemmatized_str             360808 non-null  object
dtypes: int64(3), object(8)
memory usage: 30.3+ MB

From the imported data, we can see that the data is formed by title, author, school, sentence length, tokenized text and lemmatized_str. And there is no null data, hence we can think that the data is reliable to use without further processing.

pd.DataFrame(data.groupby(by=['school','author'])['title'].count())

		title
school	author
analytic	Kripke	12479
	Lewis	13120
	Moore	3668
	Popper	4678
	Quine	7373
	Russell	5073
	Wittgenstein	9034
aristotle	Aristotle	48779
capitalism	Keynes	3411
	Ricardo	3090
	Smith	11693
communism	Lenin	4469
	Marx	13489
continental	Deleuze	12540
	Derrida	5999
	Foucault	15240
empiricism	Berkeley	2734
	Hume	8312
	Locke	8885
feminism	Beauvoir	13017
	Davis	3059
	Wollstonecraft	2559
german_idealism	Fichte	5308
	Hegel	22700
	Kant	14128
nietzsche	Nietzsche	13548
phenomenology	Heidegger	15239
	Husserl	5742
	Merleau-Ponty	7592
plato	Plato	38366
rationalism	Descartes	1132
	Leibniz	5027
	Malebranche	12997
	Spinoza	3793
stoicism	Epictetus	323
	Marcus Aurelius	2212

And then we can see the composition of all the school including the author of the respective school.

Method & Analysis

Our core method to analyze the data is to use EDA. Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods[2]. In this part, we will elicit some questions first and then use EDA to give a direct analyis about the question with some explanation.

Questions and Answer

(a) Question 1: What is the popularity of ideological schools in the dataset ？

The reason why the question is posted is because from the raw data, we can see the different groups of ideologies. Therefore, we want to know which school, author or title is used most, which can help us to reflect current ideological mainstream to some degrees. In this section, we will use bar plot to show the output to analyze the question, which can intuitively and clearly reflect the number of distributions.

# Distributions

#Title

plt.figure(figsize=(15,5))
data['title'].value_counts().plot(kind='bar', color='green')
plt.title('The distribution of the number of times the title has been cited')
plt.legend(['The number of times the title has been cited'])
plt.grid()
plt.show()
plt.savefig('1.png')
print("The group with maximum number", data['author'].value_counts().max()) # Most cited
print("The number of the mean:", data['title'].value_counts().mean())   # Average level

The group with maximum number 48779
The number of the mean: 6115.389830508475

<Figure size 432x288 with 0 Axes>

# color plot with exact number

df = data.groupby('title').size().reset_index(name='counts')
n = df['title'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)

# Plot Bars
plt.figure(figsize=(20,10), dpi= 80)
plt.bar(df['title'], df['counts'], color=c, width=.5)
for i, value in enumerate(df['counts'].values):
    plt.text(i, value, float(value), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})

# Decoration
plt.title("Frequency by Titles", fontsize=20)
plt.xlabel('Title', fontsize=15)
plt.ylabel('Frequency')
plt.show()
plt.savefig('2.png')

<Figure size 432x288 with 0 Axes>

# Author
plt.figure(figsize=(15,5))
data['author'].value_counts().plot(kind='bar', color='blue')
plt.title('The distribution of the number of times the author has been cited')
plt.legend(['The number of times the author has been cited'])
plt.grid()
plt.savefig('3.png')
plt.show()

print("The group with maximum number", data['author'].value_counts().max()) # Most cited
print("The number of the mean:", data['author'].value_counts().mean())  # Average level

The group with maximum number 48779
The number of the mean: 10022.444444444445

# color plot with exact number

df = data.groupby('author').size().reset_index(name='counts')
n = df['author'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)

# Plot Bars
plt.figure(figsize=(20,10), dpi= 80)
plt.bar(df['author'], df['counts'], color=c, width=.5)
for i, value in enumerate(df['counts'].values):
    plt.text(i, value, float(value), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})

# Decoration
plt.title("Frequency by Authors", fontsize=20)
plt.xlabel('Author', fontsize=15)
plt.ylabel('Frequency')
plt.savefig('4.png')
plt.show()

# School

plt.figure(figsize=(15,5))
data['school'].value_counts().plot(kind='bar', color='grey')
plt.title('The distribution of the number of times school has been cited')
plt.legend(['The number of times the school has been cited'])
plt.grid()
plt.savefig('5.png')
plt.show()

print("The group with maximum number", data['school'].value_counts().max()) # Most cited
print("The number of the mean:", data['school'].value_counts().mean())  # Average level

The group with maximum number 55425
The number of the mean: 27754.46153846154

# color plots with exact number:

# Prepare Data
df = data.groupby('school').size().reset_index(name='counts')
n = df['school'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)

# Plot Bars
plt.figure(figsize=(15,5), dpi= 80)
plt.bar(df['school'], df['counts'], color=c, width=.5)
for i, value in enumerate(df['counts'].values):
    plt.text(i, value, float(value), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})

# Decoration
plt.title("Frequency by Schools", fontsize=20)
plt.xlabel('Schools', fontsize=15)
plt.ylabel('Frequency')
plt.savefig('6.png')
plt.show()

From the distribution plot, we can see that the most popular ideology is about Analytic--Aristotle--Aristotle (completed work) . Hence we may see that Analytic school and Aristotle is the most mainstream in the dataset.

(b) Question 2: Which philosopher school is the most quite and which school likes to speak more ?

In this section, we may want to investigate the speaking length of philosophers, that is, we may want to find which school like to speak more and which school prefers to speak a liitle.

We will focus on the length of sentence and the token texts by school to do analysis and then deduce their speaking hlength, because the length of speaking/tokens is a significant feature of a person's speaking preference, and it can also reflect a person's personality to a certain extent.

data['n_tokens'] = list(map(len,map(eval,data.tokenized_txt)))

print(data.sentence_length.describe())

plt.figure(figsize=(12,5))
data.sentence_length.plot(kind='hist', bins=200, color='orange')
plt.title('Sentence Length')
plt.grid()
plt.savefig('7.png')
plt.show()

count    360808.000000
mean        150.790964
std         104.822072
min          20.000000
25%          75.000000
50%         127.000000
75%         199.000000
max        2649.000000
Name: sentence_length, dtype: float64

We can roughly see the shape of the histogram, however, the graph is too skewed, hence we can do log to the variable.

plt.figure(figsize=(12,5))
np.log10(data.sentence_length).plot(kind='hist', bins=50, color='pink')
plt.title('log(Sentence Length)') # Do log 10 here
plt.grid()
plt.savefig('8.png')
plt.show()

print("The mean of sentence length:", data.sentence_length.mean())

The mean of sentence length: 150.79096361499745

Based on the graph, we can see that the max length is 2649 and the min length is 20, moreover, the mean is 150, which can be regarded as a standard to judge the sentence length.

After showing the data information about sentence length, we then analyze the data information about 'tokens'.

print(data.n_tokens.describe())

plt.figure(figsize=(12,5))
data.n_tokens.plot(kind='hist', bins = 250)
plt.title('Number of Tokens')
plt.grid()
plt.savefig('9.png')
plt.show()

count    360808.000000
mean         25.693216
std          17.766261
min           0.000000
25%          13.000000
50%          22.000000
75%          34.000000
max         398.000000
Name: n_tokens, dtype: float64

We then can find that the 'Number of Tokens' mainly concentrated when number is around 25, which is the mean value. And the frequency on both sides of the peak shows a decreasing trend.

And then we will investigate the number of sentence length and Tokens by school with violin and box plot.

# violin plot sentence length split by school, violinplot

plt.figure(figsize=(13,10), dpi= 80)
sns.violinplot(x='school', y='sentence_length', data=data, scale='width', inner='quartile')
plt.title('Violin Plot of Sentence Length - By School', fontsize=20)
plt.grid()
plt.savefig('10.png')
plt.show()

# plot sentence length split by school, boxplot

    
    # Draw Plot
plt.figure(figsize=(13,10), dpi= 80)
sns.boxplot(x='school', y='sentence_length', data=data)
sns.stripplot(x='school', y='sentence_length', data=data, color='black', size=3, jitter=1)
    
for i in range(len(data['school'].unique())-1):
    plt.vlines(i+.5, 10, 45, linestyles='solid', colors='gray', alpha=0.2)
    
    # Decoration
plt.title('Box Plot of sentence length by school', fontsize=20)
plt.savefig('11.png')
plt.show()

# plot number of tokens split by school, violinplot


plt.figure(figsize=(13,10), dpi= 80)
sns.violinplot(x='school', y='n_tokens', data=data, scale='width', inner='quartile')
plt.title('Violin Plot of Number of Tokens - By School', fontsize=20)
plt.grid()
plt.savefig('12.png')
plt.show()

plt.figure(figsize=(13,10), dpi= 80)
sns.boxplot(x='school', y='n_tokens', data=data, notch=False)
    
    # Add N Obs inside boxplot (optional)
def add_n_obs(data,group_col,y):
    medians_dict = {grp[0]:grp[1][y].median() for grp in data.groupby(group_col)}
    xticklabels = [x.get_text() for x in plt.gca().get_xticklabels()]
    n_obs = data.groupby(group_col)[y].size().values
    for (x, xticklabel), n_ob in zip(enumerate(xticklabels), n_obs):
        plt.text(x, medians_dict[xticklabel]*1.01, "#obs : "+str(n_ob), horizontalalignment='center', fontdict={'size':14}, color='white')
    
    add_n_obs(data,group_col='school',y='n_tokens')    
    
    # Decoration
plt.title('Box Plot of n_tokens length by school', fontsize=20)
plt.ylim(0, 410)
plt.savefig('13.png')
plt.show()

From the violin and box graph, on the one hand, we can find that no matter numbers of tokens and sentence length, continental tends to be the most, which reflects that the school of continental prefers to speak or cite more. On the other hand, plato school is the most quite class among all schools, which may reflect that they would like to consider more but speak less.

(c) Question 3: What are the speaking habits of philosophers from different schools ?

In this section, we will investigate which words the philosophers like to speak most by school.

We firstly use wordcloud to see the words each school used. The bigger the words on the graph is, the more frequency the words are used.

c-1. Words that different genres like to say.

schools = data.school.unique().tolist()

stopwords = set(STOPWORDS)
for sc in schools:
    data_temp = data[data.school == sc]
    
    print('School = ', sc.upper(), ':')
    
    text = " ".join(txt for txt in data_temp.sentence_lowered)
    wordcloud = WordCloud(stopwords=stopwords, max_font_size=50, max_words=500,
                          width = 600, height = 400,
                          background_color="white").generate(text)
    
    plt.figure(figsize=(12,8))
    plt.imshow(wordcloud, interpolation="bilinear")
    plt.axis("off")
    plt.savefig('14.png')
    plt.show()

School =  PLATO :

School =  ARISTOTLE :

School =  EMPIRICISM :

School =  RATIONALISM :

School =  ANALYTIC :

School =  CONTINENTAL :

School =  PHENOMENOLOGY :

School =  GERMAN_IDEALISM :

School =  COMMUNISM :

School =  CAPITALISM :

School =  STOICISM :

School =  NIETZSCHE :

School =  FEMINISM :

We can then find the words for each school likes to speak more:

• PLATO : will, thing, one, socrates...
• ARISTOTLE : thing, animal, case...
• EMPIRICISM : principle, relation, mind...
• RATIONLISM : reason, cause, sensation...
• ANALYTIC : propostion, statement, case...
• CONTINENTAL: difference, madness, representation...
• PHENOMENOLOGY : consciousness, perception, object...
• GERMAN_IDEALISM : consciousness, determination, concept...
• COMMUNISM : capital, commodity, labour, value...
• CAPITALISM : price, money, country, employment...
• STOICISM : thing, thou, doth...
• NIETZSCHE : even, man, zarathustra...
• FEMINISM : women, will, marriage...

From their often used words, we can see their characters to some extents. For example, Capitalism likes to mention "employment", "money" , however communism prefers to talk about labour, commodity.

In addition, the frequency of used words among all the schools is also an important point to figure out the habits of philosophers.

c-2. Words that all genres like to say

from numpy import *
import operator

dic = {}

for i in data['tokenized_txt']:
    dic1 = eval(i)  # Transfer format
    for j in dic1:
        if j not in dic:
            dic[j] = 1
        else: 
            dic[j] = dic[j] + 1

output = sorted(dic.items(),key=operator.itemgetter(1),reverse=True)          
part_output = sorted(dic.items(),key=operator.itemgetter(1),reverse=True)[:300]
print(part_output)

[('the', 660444), ('of', 422626), ('and', 271548), ('to', 260476), ('is', 235136), ('in', 220195), ('that', 175098), ('it', 153145), ('as', 106479), ('be', 94540), ('for', 86780), ('not', 83436), ('which', 81708), ('this', 79114), ('are', 72531), ('or', 67067), ('but', 66023), ('we', 60538), ('by', 60455), ('with', 53765), ('one', 48216), ('from', 47132), ('they', 45628), ('have', 45531), ('all', 42813), ('if', 40284), ('an', 39859), ('its', 39817), ('he', 39492), ('on', 38719), ('what', 37370), ('at', 34045), ('their', 33295), ('so', 32381), ('has', 30998), ('other', 30839), ('only', 30238), ('them', 28125), ('there', 27979), ('will', 27960), ('no', 27320), ('can', 27124), ('his', 27010), ('being', 26782), ('would', 24171), ('itself', 23896), ('was', 23866), ('more', 23498), ('when', 23162), ('these', 22890), ('same', 22236), ('such', 22102), ('any', 22071), ('must', 21140), ('who', 20703), ('our', 20647), ('than', 20461), ('you', 19642), ('do', 18738), ('then', 18580), ('into', 18123), ('some', 17727), ('things', 17482), ('those', 16881), ('also', 16781), ('may', 16298), ('man', 16152), ('does', 15367), ('time', 15307), ('been', 15280), ('because', 15165), ('us', 15100), ('even', 14461), ('were', 14366), ('first', 13880), ('should', 13051), ('now', 13041), ('my', 12918), ('say', 12856), ('way', 12854), ('nature', 12796), ('two', 12474), ('about', 12240), ('world', 12088), ('therefore', 11722), ('reason', 11464), ('had', 11462), ('without', 11432), ('own', 11333), ('thing', 11275), ('him', 11243), ('something', 10841), ('thus', 10482), ('another', 10466), ('very', 10378), ('most', 10336), ('out', 10302), ('like', 10265), ('good', 10214), ('through', 10090), ('since', 10011), ('part', 9838), ('case', 9596), ('each', 9459), ('true', 9435), ('just', 9422), ('her', 9422), ('both', 9354), ('between', 9342), ('every', 9336), ('me', 9313), ('form', 9262), ('sense', 9255), ('upon', 9181), ('how', 9178), ('body', 9047), ('cannot', 8976), ('men', 8918), ('object', 8848), ('different', 8828), ('could', 8716), ('up', 8639), ('make', 8605), ('said', 8514), ('she', 8510), ('possible', 8432), ('well', 8354), ('always', 8334), ('nothing', 8279), ('knowledge', 8117), ('fact', 8051), ('certain', 7931), ('here', 7899), ('see', 7839), ('many', 7723), ('yet', 7700), ('general', 7682), ('know', 7602), ('truth', 7599), ('whole', 7581), ('means', 7551), ('however', 7519), ('life', 7496), ('think', 7466), ('thought', 7434), ('much', 7431), ('order', 7431), ('themselves', 7419), ('self', 7367), ('great', 7321), ('others', 7278), ('either', 7261), ('present', 7250), ('power', 7235), ('though', 7207), ('where', 7170), ('nor', 7117), ('whether', 7091), ('god', 7045), ('given', 7016), ('idea', 6932), ('state', 6921), ('still', 6920), ('mind', 6897), ('people', 6881), ('made', 6773), ('kind', 6651), ('might', 6611), ('existence', 6603), ('too', 6590), ('ideas', 6414), ('place', 6392), ('use', 6376), ('before', 6371), ('value', 6337), ('particular', 6318), ('never', 6315), ('relation', 6233), ('point', 6206), ('question', 6182), ('matter', 6152), ('himself', 6145), ('natural', 6136), ('far', 6116), ('over', 6004), ('necessary', 5985), ('subject', 5953), ('rather', 5919), ('after', 5907), ('consciousness', 5871), ('concept', 5848), ('experience', 5730), ('within', 5727), ('take', 5702), ('less', 5691), ('while', 5569), ('labour', 5524), ('under', 5500), ('right', 5471), ('called', 5441), ('again', 5425), ('why', 5387), ('cause', 5343), ('already', 5298), ('parts', 5201), ('hand', 5167), ('law', 5131), ('against', 5114), ('objects', 5114), ('soul', 5106), ('human', 5033), ('let', 4989), ('new', 4950), ('come', 4932), ('shall', 4929), ('work', 4885), ('become', 4872), ('according', 4830), ('give', 4754), ('number', 4724), ('am', 4687), ('merely', 4666), ('end', 4634), ('words', 4621), ('language', 4588), ('difference', 4576), ('makes', 4565), ('principle', 4434), ('pure', 4408), ('real', 4355), ('find', 4354), ('motion', 4347), ('seems', 4288), ('meaning', 4251), ('movement', 4198), ('did', 4185), ('anything', 4173), ('example', 4140), ('perhaps', 4128), ('universal', 4117), ('love', 4099), ('understanding', 4099), ('common', 4085), ('else', 4079), ('contrary', 4073), ('long', 4068), ('indeed', 4058), ('your', 4050), ('view', 4024), ('having', 4019), ('neither', 4005), ('greater', 4004), ('further', 4001), ('latter', 3986), ('women', 3981), ('everything', 3969), ('together', 3940), ('terms', 3876), ('person', 3869), ('woman', 3839), ('found', 3834), ('above', 3831), ('mean', 3817), ('clear', 3776), ('money', 3764), ('name', 3756), ('among', 3742), ('word', 3734), ('whose', 3729), ('little', 3724), ('laws', 3714), ('call', 3707), ('science', 3666), ('essence', 3663), ('taken', 3642), ('least', 3605), ('second', 3602), ('simple', 3587), ('animals', 3568), ('once', 3566), ('able', 3541), ('produce', 3537), ('theory', 3515), ('cases', 3500), ('longer', 3495), ('course', 3482), ('capital', 3472), ('sort', 3451), ('often', 3418), ('hence', 3418), ('unity', 3393), ('becomes', 3389), ('system', 3376), ('bodies', 3315), ('individual', 3294), ('free', 3288), ('pleasure', 3288), ('better', 3279), ('property', 3279), ('ever', 3274)]

And then we will use plot to show the part of output.

plt.figure(figsize=(30,30))
data_frame = pd.DataFrame(data=output[0:19],columns=["words","Frequency"])
data_frame.plot(x = "words", y = "Frequency", kind='bar', color='red')
plt.savefig('15.png')
plt.show()

<Figure size 2160x2160 with 0 Axes>

We can see that the most frequent words are all Pronouns, conjunctions and prepositions or Copula and Modal verbs, which is a lack of meaning. Hence by obeservation, we find when around 150th frequency, the words begin to have characters.

data_frame = pd.DataFrame(data=output[150:180],columns=["words","Frequency"])
data_frame.plot(x = "words", y = "Frequency", kind='bar', color = "gray")
plt.figure(figsize=(30,30))
plt.savefig('16.png')
plt.show()

<Figure size 2160x2160 with 0 Axes>

data_frame = pd.DataFrame(data=output[0:20],columns=["words","Frequency"])
data_frame.plot(x="words", y = "Frequency", kind='pie')
plt.savefig('17.png')

We also use pie chart to show the Proportion about the top 20 used words.

From the graph and print output, we can see that except Pronouns, conjunctions and prepositions or Copula and Modal verbs, philosophers also like to say power, god, mind, people, and existent and so on.

Conclusion

1. We find that Analytic--Aristotle--Aristotle (completed work) may be the most popular school/author/title, because its frequence is ranked first among all the school.

2. We find that continental tends to speak the most but plato school is the most quite school.

3. Every school has its unique speaking preference and prefered words, but they all may prefer to mention about people, god, mind, power and existent and so on.

Development

1. In this section, we do not analyze the conclusion with its background, that is, we do not link the data output with the real world background.
2. There is no in-depth exploration of what factors can affect the length of speech and other issues.
3. For c-2 parts, it is possible that some schools dominate a word that may not be used by all schools.

Reference

[1] [https://en.wikipedia.org/wiki/Ideology]

[2] [https://www.ibm.com/cloud/learn/exploratory-data-analysis]